Making a Better Algorithm: Machine Learning & Biology


Michael Goldberg of Data Informed reports, "When Lars Hård, an artificial intelligence veteran, discusses developing a recommendation engine for a consumer product like perfume, he talks about harvesting many kinds of data. Product data. Consumer search engine data. Design data having to do with colors that men or women prefer. Pricing data. To Hård, founder and chief technology officer at Expertmaker, data in all its structured and unstructured forms represents signals that machine learning systems pick up to refine results for future interactions. The data, and those interactions, also figure into Hård’s work of applying principles of evolutionary biology to both business use cases, like shopping assistants, and medical research."

Goldberg continues, "On this episode of the Data Informed podcast, Hård discusses how his work seeks to build on the heritage of the famous “if you like Book A, you might enjoy Book B” screen at  He describes the process of working with very large datasets from structured and unstructured sources to develop what he calls an optimized map of data correlations that will lead to end-user action… He also discusses how he has adopted the principles of evolutionary biology to tackle other big problems. For example, he has established another company, called Experlytics, which seeks to apply artificial intelligence models to medical research, to pinpoint promising correlations and directions for researchers to explore."

Listen to the podcast here.

Image: Courtesy Expertmaker